It is easy to see how the results from this study could be used to understand, and even predict, patterns and anomalies in an occupant's environmental preferences in flexible workspaces over time. Taking this a step further, learning from past comfort preferences of occupants could be used to match them to spaces with suitable environmental profiles with acceptable temperature, light, and noise levels on average. This process can be done in real-time using IoT data to test whether this leads to an increase in occupant satisfaction or performance in flexible workplaces compared to a baseline scenario. Such methods of suggesting or matching based on past preferences have been widely used in other industries such as media and social networking [1]}, [2]}, [3]}, but they are still a new concept for the built environment.
